Infrared imaging techniques have extensive and important applications in military and civilian fields. With the development and popularization of infrared imaging techniques, network transmission for infrared images has become the foundation for numerous applications and meanwhile the bottleneck for the development of these applications.
For promoting the application of infrared techniques, an urgent problem to be solved is to improve the transmission efficiency for the infrared images under a limited bandwidth. Data compression techniques based on compressive sensing (CS) have been a new direction in the field of data compression in recent years.
In view of the current research at home and abroad, applications of compressive sensing techniques in image processing mostly focus on an image itself for sensing of compression and reconstruction without considering the temporal correlation in time sequence between consecutive frames. However, such temporal correlation is common for sequential images in practice. Therefore, compressive sensing methods in the prior art have the defects of poor reconstruction precision and low compression efficiency.